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update doc

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jhqxxx
2026-05-24 19:26:47 +08:00
parent 925e5dd7c0
commit 754145246c
8 changed files with 291 additions and 101 deletions
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README.md
+10 -9
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@@ -33,12 +33,15 @@ aha is a high-performance, cross-platform AI inference engine built with Rust an
| **Vision** | Qwen2.5-VL, Qwen3-VL, Qwen3.5, <br> LFM2.5-VL, LFM2-VL |
| **OCR** | DeepSeek-OCR, DeepSeek-OCR-2 , PaddleOCR-VL <br> PaddleOCR-VL1.5, Hunyuan-OCR, GLM-OCR |
| **ASR** | GLM-ASR-Nano, Fun-ASR-Nano, Qwen3-ASR |
| **TTS** | VoxCPM, VoxCPM1.5, VoxCPM2 |
| **TTS** | VoxCPM, VoxCPM1.5, VoxCPM2, Moss-TTS-Nano |
| **Image** | RMBG-2.0 (background removal) |
| **Embedding** | Qwen3-Embedding, all-MiniLM-L6-v2 |
| **Reranker** | Qwen3-Reranker |
## Changelog
### 2026-05-24
- update doc
### 2026-05-11
- add Moss-TTS-Nano，its performance is worse than the original Python version
@@ -215,27 +218,25 @@ pnpm run tauri build
```rust
# VoxCPM example
use aha::models::voxcpm::generate::VoxCPMGenerate;
use aha::utils::audio_utils::save_wav;
use aha::utils::audio_utils::save_wav_mono;
use anyhow::Result;
fn main() -> Result<()> {
let model_path = "xxx/openbmb/VoxCPM-0.5B/";
let model_path = "xxx/OpenBMB/VoxCPM2/";
let mut voxcpm_generate = VoxCPMGenerate::init(model_path, None, None)?;
let generate = voxcpm_generate.generate(
"The sun is shining bright, flowers smile at me, birds say early early early".to_string(),
let generate = voxcpm_generate.inference(
"aha是一个基于Rust和Candle框架的本地AI推理引擎，支持多模态模型（文本、视觉、语音、OCR）。".to_string(),
None,
None,
2,
100,
1000,
10,
2.0,
false,
6.0,
)?;
let _ = save_wav(&generate, "voxcpm.wav")?;
save_wav_mono(&generate, "voxcpm2.wav", voxcpm_generate.sample_rate() as u32)?;
Ok(())
}
```
README.zh-CN.md
+9 -8
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@@ -38,6 +38,9 @@ aha 是一款基于 Rust 和 Candle 框架构建的高性能跨平台 AI 推理
| **重排序** | Qwen3-Reranker |
## 更新日志
### 2026-05-24
- 更新文档
### 2026-05-11
- 添加Moss-TTS-Nano，该模型比较小，计算误差影响较大，效果比python原版差
@@ -214,27 +217,25 @@ pnpm run tauri build
```rust
# VoxCPM示例
use aha::models::voxcpm::generate::VoxCPMGenerate;
use aha::utils::audio_utils::save_wav;
use aha::utils::audio_utils::save_wav_mono;
use anyhow::Result;
fn main() -> Result<()> {
let model_path = "xxx/openbmb/VoxCPM-0.5B/";
let model_path = "xxx/OpenBMB/VoxCPM2/";
let mut voxcpm_generate = VoxCPMGenerate::init(model_path, None, None)?;
let generate = voxcpm_generate.generate(
"太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
let generate = voxcpm_generate.inference(
"aha是一个基于Rust和Candle框架的本地AI推理引擎，支持多模态模型（文本、视觉、语音、OCR）。".to_string(),
None,
None,
2,
100,
1000,
10,
2.0,
false,
6.0,
)?;
let _ = save_wav(&generate, "voxcpm.wav")?;
save_wav_mono(&generate, "voxcpm2.wav", voxcpm_generate.sample_rate() as u32)?;
Ok(())
}
```
assets/img/aha_weixinqun.png
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src/models/voxcpm_refact/generate.rs
+159 -9
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@@ -1,11 +1,13 @@
use anyhow::{Result, anyhow};
use base64::{Engine, prelude::BASE64_STANDARD};
use candle_core::{DType, Device, Tensor, pickle::read_all_with_key};
use candle_nn::VarBuilder;
use rocket::futures::Stream;
use rocket::futures::{Stream, stream};
use std::collections::HashMap;
use crate::{
models::{
GenerateModel,
voxcpm::{
audio_vae::AudioVAE,
config::{AudioVaeConfig, VoxCPMConfig},
@@ -13,7 +15,12 @@ use crate::{
},
voxcpm_refact::{model::VoxCPMModelRefact, processor::VoxCPMProcessor},
},
utils::{find_type_files, get_device, get_dtype},
params::chat::{ChatCompletionChunkResponse, ChatCompletionParameters, ChatCompletionResponse},
utils::{
audio_utils::{extract_audio_url, get_audio_wav_u8},
extract_metadata_value, extract_user_text, find_type_files, get_device, get_dtype,
response_utils::build_audio_completion_response,
},
};
pub struct VoxCPMGenerateRefact {
@@ -23,7 +30,7 @@ pub struct VoxCPMGenerateRefact {
processor: VoxCPMProcessor,
prompt_cache: Option<HashMap<String, Tensor>>,
out_sample_rate: usize,
// model_name: String,
model_name: String,
}
impl VoxCPMGenerateRefact {
@@ -55,11 +62,11 @@ impl VoxCPMGenerateRefact {
sr_bin_boundaries: None,
},
};
// let model_name = std::path::Path::new(path)
// .file_name()
// .and_then(|s| s.to_str())
// .unwrap_or("VoxCPM")
// .to_string();
let model_name = std::path::Path::new(path)
.file_name()
.and_then(|s| s.to_str())
.unwrap_or("VoxCPM")
.to_string();
let audio_vae = AudioVAE::new(
vb_vae,
audio_config.encoder_dim,
@@ -118,7 +125,7 @@ impl VoxCPMGenerateRefact {
processor,
prompt_cache: None,
out_sample_rate,
// model_name,
model_name,
})
}
@@ -126,6 +133,73 @@ impl VoxCPMGenerateRefact {
self.out_sample_rate
}
pub fn inference(
&mut self,
target_text: String,
prompt_text: Option<String>,
prompt_wav_path: Option<String>,
min_len: usize,
max_len: usize,
inference_timesteps: usize,
cfg_value: f64,
retry_badcase: bool,
retry_badcase_ratio_threshold: f64,
) -> Result<Tensor> {
let (text_token, audio_feat, audio_mask) = self.processor.processor(
target_text,
prompt_text,
prompt_wav_path,
&self.tokenizer,
&self.audio_vae,
)?;
let target_text_length = if let Some(mask) = &audio_mask {
text_token.dim(1)? - (mask.sum_all()?.to_scalar::<u32>()? as usize)
} else {
text_token.dim(1)?
};
let max_len = if retry_badcase {
(target_text_length as f64 * retry_badcase_ratio_threshold + 10.0) as usize
} else {
max_len
};
let audio = self.voxcpm.inference(
&text_token,
audio_feat.as_ref(),
audio_mask.as_ref(),
min_len,
max_len,
inference_timesteps,
cfg_value,
&self.audio_vae,
)?;
self.voxcpm.clear_kv_cache();
Ok(audio)
}
pub fn generate_with_prompt_simple(
&mut self,
target_text: String,
prompt_text: Option<String>,
prompt_wav_path: Option<String>,
) -> Result<Tensor> {
let audio = self.inference(
target_text,
prompt_text,
prompt_wav_path,
2,
1000,
10,
2.0,
false,
6.0,
)?;
Ok(audio)
}
pub fn generate_simple(&mut self, target_text: String) -> Result<Tensor> {
let audio = self.inference(target_text, None, None, 2, 100, 10, 2.0, false, 6.0)?;
Ok(audio)
}
pub fn build_prompt_cache(
&mut self,
prompt_text: String,
@@ -225,3 +299,79 @@ impl VoxCPMGenerateRefact {
}
}
}
impl GenerateModel for VoxCPMGenerateRefact {
fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
let prompt_text = extract_metadata_value::<String>(&mes.metadata, "prompt_text");
let control_instruction =
extract_metadata_value::<String>(&mes.metadata, "control_instruction");
let min_len = extract_metadata_value::<usize>(&mes.metadata, "min_len").unwrap_or(2);
let max_len = extract_metadata_value::<usize>(&mes.metadata, "max_len").unwrap_or(4096);
let inference_timesteps =
extract_metadata_value::<usize>(&mes.metadata, "inference_timesteps").unwrap_or(10);
let cfg_value = extract_metadata_value::<f64>(&mes.metadata, "cfg_value").unwrap_or(2.0);
let retry_badcase_ratio_threshold =
extract_metadata_value::<f64>(&mes.metadata, "retry_badcase_ratio_threshold")
.unwrap_or(6.0);
let prompt_wav = extract_audio_url(&mes);
let prompt_wav_path = if !prompt_wav.is_empty() {
Some(prompt_wav[0].clone())
} else {
None
};
if !self.model_name.contains("2") && prompt_wav_path.is_some() && prompt_text.is_none() {
return Err(anyhow!(
"reference mode is only supported with VoxCPM2 models"
));
}
let mut target_text = extract_user_text(&mes)?;
if let Some(instruction) = control_instruction
&& self.model_name.contains("2")
{
target_text = format!("({instruction}){target_text}");
}
let audio = self
.inference(
target_text,
prompt_text,
prompt_wav_path,
min_len,
max_len,
inference_timesteps,
cfg_value,
true,
retry_badcase_ratio_threshold,
)
.inspect_err(|_| {
self.voxcpm.clear_kv_cache();
})?;
let wav_u8 = get_audio_wav_u8(&audio, self.out_sample_rate as u32)?;
// let wave_u8_str = String::from_utf8(wav_u8)?;
let base64_audio = BASE64_STANDARD.encode(wav_u8);
let response = build_audio_completion_response(&base64_audio, &self.model_name);
self.voxcpm.clear_kv_cache();
Ok(response)
}
#[allow(unused_variables)]
fn generate_stream(
&mut self,
mes: ChatCompletionParameters,
) -> Result<
Box<
dyn Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>
+ Send
+ Unpin
+ '_,
>,
> {
let error_stream = stream::once(async {
Err(anyhow::anyhow!(format!(
"{} model not support stream",
self.model_name
))) as Result<ChatCompletionChunkResponse, anyhow::Error>
});
Ok(Box::new(Box::pin(error_stream)))
}
}
src/models/voxcpm_refact/model.rs
-3
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@@ -196,9 +196,6 @@ impl VoxCPMModelRefact {
(text_embed, prefix_feat_cond, None)
};
let mut pred_feat_seq = Vec::new();
// if feat_mask.i((1, t-1))?.to_scalar::<f32>()? == 0.0 {
// // TODO for stream
// }
let mut position_id = 0;
let mut seq_len = t;
let enc_outputs = self
tests/test_voxcpm.rs
+29 -32
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@@ -1,18 +1,16 @@
use std::time::Instant;
use aha::models::voxcpm_refact::generate::VoxCPMGenerateRefact;
use aha::params::chat::ChatCompletionParameters;
use aha::{
models::{
GenerateModel,
voxcpm::{generate::VoxCPMGenerate, tokenizer::SingleChineseTokenizer},
},
models::{GenerateModel, voxcpm::tokenizer::SingleChineseTokenizer},
utils::audio_utils::{extract_and_save_audio_from_response, save_wav_mono},
};
use anyhow::{Ok, Result};
#[test]
fn voxcpm_use_message_generate() -> Result<()> {
// RUST_BACKTRACE=1 cargo test -F cuda voxcpm_use_message_generate -r -- --nocapture
// RUST_BACKTRACE=1 cargo test -F cuda --test test_voxcpm voxcpm_use_message_generate -r -- --nocapture
let save_dir =
aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
let model_path = format!("{}/OpenBMB/VoxCPM-0.5B/", save_dir);
@@ -42,7 +40,8 @@ fn voxcpm_use_message_generate() -> Result<()> {
"#;
let mes: ChatCompletionParameters = serde_json::from_str(message)?;
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
// let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
let mut voxcpm_generate = VoxCPMGenerateRefact::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
@@ -54,7 +53,6 @@ fn voxcpm_use_message_generate() -> Result<()> {
}
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
// save_wav_mono(&generate, "voxcpm.wav", 16000)?;
Ok(())
}
@@ -66,35 +64,16 @@ fn voxcpm_generate() -> Result<()> {
let model_path = format!("{}/OpenBMB/VoxCPM-0.5B/", save_dir);
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
let mut voxcpm_generate = VoxCPMGenerateRefact::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
// let i_start = Instant::now();
// let generate = voxcpm_generate.generate_simple("太阳当空照，花儿对我笑，小鸟说早早早".to_string())?;
// let generate = voxcpm_generate.inference(
// "VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.".to_string(),
// Some("啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string()),
// Some("file://./assets/audio/voice_01.wav".to_string()),
// // Some("一定被灰太狼给吃了，我已经为他准备好了花圈了".to_string()),
// // Some("file://./assets/audio/voice_05.wav".to_string()),
// 2,
// 100,
// 10,
// 2.0,
// // false,
// 6.0,
// )?;
// 创建prompt_cache
voxcpm_generate.build_prompt_cache(
"啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
"file://./assets/audio/voice_01.wav".to_string(),
)?;
// 使用prompt_cache生成语音
let i_start = Instant::now();
let generate = voxcpm_generate.generate_use_prompt_cache(
"太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
// let generate = voxcpm_generate.generate_simple("太阳当空照，花儿对我笑，小鸟说早早早".to_string())?;
let generate = voxcpm_generate.inference(
"VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.".to_string(),
Some("天雷滚滚我好怕怕，劈得我浑身掉渣渣。突破天劫我笑哈哈，逆天改命我吹喇叭，滴答滴答滴滴答".to_string()),
Some("https://package-release.coderbox.cn/aiway/test/other/%E5%93%AA%E5%90%92.wav".to_string()),
2,
100,
10,
@@ -103,6 +82,23 @@ fn voxcpm_generate() -> Result<()> {
6.0,
)?;
// 创建prompt_cache
// voxcpm_generate.build_prompt_cache(
// "啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
// "file://./assets/audio/voice_01.wav".to_string(),
// )?;
// // 使用prompt_cache生成语音
// let i_start = Instant::now();
// let generate = voxcpm_generate.generate_use_prompt_cache(
// "太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
// 2,
// 100,
// 10,
// 2.0,
// false,
// 6.0,
// )?;
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
save_wav_mono(&generate, "voxcpm.wav", 16000)?;
@@ -111,6 +107,7 @@ fn voxcpm_generate() -> Result<()> {
#[test]
fn voxcpm_tokenizer() -> Result<()> {
// RUST_BACKTRACE=1 cargo test -F cuda --test test_voxcpm voxcpm_tokenizer -r -- --nocapture
let save_dir =
aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
let model_path = format!("{}/OpenBMB/VoxCPM-0.5B/", save_dir);
tests/test_voxcpm1_5.rs
+23 -30
View File
@@ -3,10 +3,7 @@ use std::time::Instant;
use aha::models::voxcpm_refact::generate::VoxCPMGenerateRefact;
use aha::params::chat::ChatCompletionParameters;
use aha::{
models::{
GenerateModel,
voxcpm::{generate::VoxCPMGenerate, tokenizer::SingleChineseTokenizer},
},
models::{GenerateModel, voxcpm::tokenizer::SingleChineseTokenizer},
utils::audio_utils::{extract_and_save_audio_from_response, save_wav_mono},
};
use anyhow::{Ok, Result};
@@ -43,7 +40,7 @@ fn voxcpm1_5_use_message_generate() -> Result<()> {
"#;
let mes: ChatCompletionParameters = serde_json::from_str(message)?;
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
let mut voxcpm_generate = VoxCPMGenerateRefact::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
@@ -66,43 +63,39 @@ fn voxcpm1_5_generate() -> Result<()> {
let model_path = format!("{}/OpenBMB/VoxCPM1.5/", save_dir);
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
let mut voxcpm_generate = VoxCPMGenerateRefact::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
let i_start = Instant::now();
// let generate = voxcpm_generate.generate_simple("太阳当空照，花儿对我笑，小鸟说早早早".to_string())?;
// let generate = voxcpm_generate.inference(
// "老大爷我来啦，红红火火恍恍惚惚".to_string(),
// Some("啥子小师叔，打狗还要看主人，你再要继续，我就是你的对手".to_string()),
// Some("file://./assets/audio/voice_01.wav".to_string()),
// // Some("一定被灰太狼给吃了，我已经为他准备好了花圈了".to_string()),
// // Some("file://./assets/audio/voice_05.wav".to_string()),
// 2,
// 4096,
// 10,
// 2.0,
// // false,
// 6.0,
// )?;
// 创建prompt_cache
voxcpm_generate.build_prompt_cache(
"啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
"file://./assets/audio/voice_01.wav".to_string(),
)?;
// 使用prompt_cache生成语音
let generate = voxcpm_generate.generate_use_prompt_cache(
"太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
let generate = voxcpm_generate.inference(
"老大爷我来啦，红红火火恍恍惚惚".to_string(),
Some("天雷滚滚我好怕怕，劈得我浑身掉渣渣。突破天劫我笑哈哈，逆天改命我吹喇叭，滴答滴答滴滴答".to_string()),
Some("https://package-release.coderbox.cn/aiway/test/other/%E5%93%AA%E5%90%92.wav".to_string()),
2,
100,
4096,
10,
2.0,
false,
6.0,
)?;
std::thread::sleep(std::time::Duration::from_secs(2));
// 创建prompt_cache
// voxcpm_generate.build_prompt_cache(
// "啥子小师叔，打狗还要看主人，你再要继续，我，就是你的对手".to_string(),
// "file://./assets/audio/voice_01.wav".to_string(),
// )?;
// // 使用prompt_cache生成语音
// let generate = voxcpm_generate.generate_use_prompt_cache(
// "太阳当空照，花儿对我笑，小鸟说早早早".to_string(),
// 2,
// 100,
// 10,
// 2.0,
// false,
// 6.0,
// )?;
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
tests/test_voxcpm2.rs
+60 -9
View File
@@ -1,9 +1,12 @@
use std::time::Instant;
use aha::{
models::{GenerateModel, voxcpm::generate::VoxCPMGenerate},
models::{
GenerateModel, voxcpm::generate::VoxCPMGenerate,
voxcpm_refact::generate::VoxCPMGenerateRefact,
},
params::chat::ChatCompletionParameters,
utils::audio_utils::extract_and_save_audio_from_response,
utils::audio_utils::{extract_and_save_audio_from_response, save_wav_mono},
};
use anyhow::Result;
@@ -30,26 +33,74 @@ fn voxcpm2_use_message_generate() -> Result<()> {
},
{
"type": "text",
"text": "你好，这是aha在说话"
"text": "aha是一个基于Rust和Candle框架的本地AI推理引擎，支持多模态模型（文本、视觉、语音、OCR）。"
}
]
}
]
],
"metadata": {"prompt_text": "天雷滚滚我好怕怕，劈得我浑身掉渣渣。突破天劫我笑哈哈，逆天改命我吹喇叭，滴答滴答滴滴答"}
}
"#;
let mes: ChatCompletionParameters = serde_json::from_str(message)?;
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerateRefact::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
// for _ in 0..10 {
// let _ = voxcpm_generate.generate(mes.clone())?;
// }
// let mut times = vec![];
// for _ in 0..100 {
// let start = Instant::now();
// let _ = voxcpm_generate.generate(mes.clone())?;
// times.push(start.elapsed());
// }
// let mean = times.iter().sum::<Duration>() / 100;
// println!("mean: {:?}", mean);
// times.sort();
// println!("p99: {:?}", times[99]);
let i_start = Instant::now();
let generate = voxcpm_generate.generate(mes)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
let save_path = extract_and_save_audio_from_response(&generate, "./")?;
for path in save_path {
println!("save audio: {}", path);
}
Ok(())
}
#[test]
fn voxcpm2_generate() -> Result<()> {
// RUST_BACKTRACE=1 cargo test -F cuda --test test_voxcpm2 voxcpm2_generate -r -- --nocapture
let save_dir =
aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
let model_path = format!("{}/OpenBMB/VoxCPM2/", save_dir);
let i_start = Instant::now();
let mut voxcpm_generate = VoxCPMGenerate::init(&model_path, None, None)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in load model is: {:?}", i_duration);
let i_start = Instant::now();
let generate = voxcpm_generate.generate(mes)?;
let save_path = extract_and_save_audio_from_response(&generate, "./")?;
for path in save_path {
println!("save audio: {}", path);
}
let generate = voxcpm_generate.inference(
"aha是一个基于Rust和Candle框架的本地AI推理引擎，支持多模态模型（文本、视觉、语音、OCR）。"
.to_string(),
None,
None,
2,
1000,
10,
2.0,
6.0,
)?;
let i_duration = i_start.elapsed();
println!("Time elapsed in generate is: {:?}", i_duration);
save_wav_mono(
&generate,
"voxcpm2.wav",
voxcpm_generate.sample_rate() as u32,
)?;
Ok(())
}